Overview
Background
This document sets out a few practical recipes for modelling with (life) insurance data. Insurance events are typically of low probability - these recipes consider some of the limitations of “small data” model fitting (where the observations of interest are sparse) and other topics for insurance like comparisons to standard tables. Themes
- Common data transforms, summary stats, and simple visualisations
- Regression
- Grouped vs ungrouped data
- Choice of: response distribution, link (and offsets), explanatory variables
- Modelling variance to industry/ reference (A/E or A - E)
- Model selection: stepwise regression, likelihood tests, model evaluation
- Predictions, confidence intervals and visualisations
- Bayesian regression and other classification models - to follow.
See link above to GitHub repository which has the detailed code.